09 Dec 2020
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Author: Vladimir Fux
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or@home
or
budget optimization
bank
data
pulp
cbc
optimization
Relative importance of transactions
For optimization to make sense, we as users, may want to specify relative importants of transactions. In the end we want to deduce a retroactive savings plan, and to do so we need to “cancel” some transactions. It is clear that one may save on grocery shopping, but saving on rent payments does not sound like a good idea.
26 Nov 2020
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Author: Vladimir Fux
|
or@home
or
budget optimization
bank
data
visualization
plotly
In this part we visualize bank transactions data, aiming to get an idea
- Types of expenses and there share
- Savings trends
- Expenses by monthes and types
Just to understand what do we spend money on, and where is potential for savings.
23 Nov 2020
|
Author: Vladimir Fux
|
or@home
or
optimization
operations research
budget optimization
bank
data
preparation
In this part we explore our bank account data, prepare it and deal with the transaction classification.
This sunbirst chart from plotly library is a sneek preview from visualization part of this post series. Our task for now is to prepare data, in order to allow such type of visualizations.
22 Nov 2020
|
Author: Vladimir Fux
|
or@home
or
optimization
operations research
budget optimization
bank
account
I was once analyzing my bank account spendings. My bank UI was not very convenient: I cannot easily filter on interesting fields, some visualization were lacking, etc. But was there is an export as .csv button, which came quite handy. I decided to look at my expenses through my favorite tools.
Photo by Morgan Housel on Unsplash
Nice! I can do whatever I want with my data! I started looking where I could have avoided spending to much money and then an idea came to my mind: this can be treated as optimization problem! So here it is: Budget optimization @HOME
08 Nov 2020
|
Author: Vladimir Fux
|
or@home
or
optimization
operations research
topic
Catching cat/dog Locomotive

Original photo by Kalden Swart on Unsplash
During my career I encountered Operations Research (O.R.) tooling in various environments: banking, supply chains, e-commerce and even fishery industries. In most of the cases I felt surprized (and sometimes frustrated) that our customers know very little to nothing about Operations Research discipline, while almost everyone knew what AI, DS and ML mean. In the best case people will assume that this is something to do with machine learning (and they will be partially correct), in the worst case they will guess that this is something to do with operations management (also not totally wrong).